Extraction of discriminant features based on optimal transformation and cluster centers of kernel space
Hongyi Zhang, Xiuwei Wu, Jiexin Pu · 2008
It has been proved a lot of linear feature extraction methods can be generalized to the nonlinear learning methods by using kernel methods. In this paper, a new nonlinear learning method of optimal transformation and cluster centers (OT-CC) is presented by using kernel technique. It is named as optimal transformation and cluster centers algorithm of kernel space (KOT-CC), which is a powerful technique for extracting nonlinear discriminant features and is very effective in solving pattern recognition problems where the overlap between patterns is serious. A large number of experiments demonstrate the new algorithm outperforms OT-CC and kernel fisher discriminant analysis (KFDA).